Conventional machine learning approaches often treat HAR as a standard time series classification problem. After getting raw data from sensors, they first preprocess the raw data which includes denoising [1]} and segmentation [2]}. Then feature extraction and feature selection [3]} are implemented to extract useful features from the pre-processed data. In the following, different models, such as random forest (RF) [4]}, Bayesian networks [5]}, and support vector machine (SVM) [6]}, [7]}, are built with selected features. Finally, we can use these learned models to make activity inferences. Deep learning based HAR [8]}, [9]}, [10]}, [11]}, [12]} automatically extracts abstract features through several hidden layers and reduces the effort of choosing the right features.
